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Course Overview: What You’ll Learn in AI Rate Limiting and API Throttling Patterns
VIBE CODING PEOPLE · COURSE OVERVIEW
AI Rate Limiting and API Throttling Patterns
A hands-on, no-fluff course built for people who want to ship. Every lesson is immediately applicable — no 40-minute intros before you do anything real.
What You Will Learn
- Map the four distinct threats every API faces and design limits that address each.
- Pick the right rate-limit algorithm (token bucket, sliding window, etc.) for your workload.
- Implement distributed rate limiting in Redis with atomic Lua-based counters.
- Turn rate limits into a pricing lever with clean tiers and self-regulating headers.
- Deploy AI-assisted anomaly detection that catches subtle abuse under static limits.
- Ship an SDK and runbook that make the whole system polite, observable, and debuggable.
Full Course Breakdown
All paid lessons included in this course:
LESSON 2
Why Rate Limiting Matters: The Four Threats Every API Must Survive
Rate limiting isn't just about fairness — it's about survival. We'll map the four distinct threats it defends against (DDoS, scraping, cost runaway, and self-inflicted traffic) and why a single limit rarely covers all of them.
LESSON 3
Algorithms That Actually Work: Token Bucket, Leaky Bucket, Sliding Window
A clear-headed comparison of the four algorithms you'll choose between: fixed window, sliding window, leaky bucket, and token bucket. Which one fits which problem, and why token bucket usually wins.
LESSON 4
Distributed Rate Limiting: Redis, Counting at Scale, and the Consistency Trap
The moment you have more than one API node, rate limiting gets hard. We'll cover Redis-backed counters, sliding-window logs, and the race conditions that make naive implementations quietly leak.
LESSON 5
Tiered Rate Limits and the Pricing-Rate-Limit Marriage
Rate limits aren't just a defense — they're your pricing lever. We'll cover free vs. paid vs. enterprise tiers, burst credits, fair-use clauses, and how to design limits that customers respect.
LESSON 6
AI-Assisted Anomaly Detection: When to Throttle Before a Limit Is Hit
Static limits catch obvious abuse. AI-driven anomaly detection catches the subtle stuff — slowly-ramping scrapers, compromised credentials, and workloads that hit the limit exactly. Here's how to deploy it responsibly.
LESSON 7
Client-Side Handling: Retry, Backoff, and the 429 Contract
Your rate limiter is only as good as the client that respects it. We'll cover 429 responses, Retry-After headers, exponential backoff with jitter, and why 'polite' SDKs make the whole system safer.
LESSON 8
Observability, Alerts, and the 'Is It Me Or Them' Problem
When usage spikes, you need to know in 90 seconds whether it's a legitimate customer, an attacker, or an internal bug. We'll build the dashboards, alerts, and runbooks that make this a 5-minute call instead of a 2-hour war room.
Who This Course Is For
- Backend engineers who own a public or semi-public API.
- Platform teams designing rate-limit services for internal use.
- SREs and on-call engineers tired of waking up to 'why is the API slow'.
- Founders and tech leads setting pricing tiers that double as rate limits.
- Engineers working on AI APIs who need to combine tokens/min, tokens/day, and cost controls.
What Makes This Course Different
- Algorithm coverage without the academic theory — only what matters for production.
- Concrete Redis + Lua patterns you can copy into a sprint.
- A full AI-anomaly-detection walkthrough, not just a hand-wave.
- The pricing-meets-rate-limit chapter most 'rate limiting guides' skip entirely.
- Hands-on exercises for every lesson, each runnable against your own API.
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